OpenRaiser/Intern-Atlas huggingface dataset:
Inductive Sparse Subspace Clustering
Venue: arXiv cs.LG 2013
Year: 2013

Sparse Subspace Clustering (SSC) has achieved state-of-the-art clustering quality by performing spectral clustering over a $\ell^{1}$-norm based similarity graph. However, SSC is a transductive method which does not handle with the data not used to construct the graph (out-of-sample data). For each new datum, SSC requires solving $n$ optimization problems in O(n) variables for performing the algorithm over the whole data set, where $n$ is the number of data points. Therefore, it is inefficient to apply SSC in fast online clustering and scalable graphing. In this letter, we propose an inductive spectral clustering algorithm, called inductive Sparse Subspace Clustering (iSSC), which makes SSC feasible to cluster out-of-sample data. iSSC adopts the assumption that high-dimensional data actually lie on the low-dimensional manifold such that out-of-sample data could be grouped in the embedding space learned from in-sample data. Experimental results show that iSSC is promising in clustering out-of-sample data.
ICT System Design & Implementation Using Wireless Sensors to Support Elderly In-home Assistance
Venue: arXiv cs.SE 2013
Year: 2013

Around the globe the number of older people in relation to the rest is constantly growing. As a result, medical and care facilities cannot handle the growing number of patients. Therefore, elderly in-home assistance gets more attention an importance. Due to issues regarding memory, physical strength and reduced self-assessment, old people face a lot of challenges in accomplishing their activities of daily living. This thesis is meant to address these problems by analysing the required infrastructure of a home-care facility as well as the arising issues regarding used components, especially wireless sensors. After the analysis, a prototype of a home-care system is designed and implemented. Furthermore, the issue of energy consumption of the used wireless sensor node is addressed by modifying the intelligence of the used sensor. After that, the design and components of the prototype used for the energy consumption analysis is explained, together with the programming structure of the sensor nodes used in this thesis. Thereupon, the results are of the simulations are discussed and compared with the authors' expectations. Finally the overall outcomes of the thesis are analysed and summed up, followed by a short outlook of further possible improvements and developments.
Methods for robust PID control
Venue: arXiv cs.SY 2013
Year: 2013

A comprehensive theory for robust PID control in continuous-time and discrete-time domain is reviewed in this paper. For a given finite set of linear time invariant plants, algorithms for fast computation of robustly stabilizing regions in the ($k_P, k_I, k_D$)-parameter space are introduced. The main impetus is given by the fact that non-convex stable regions in the PID parameter space can be built up by convex polygonal slices. A simple and an elegant theory evolved in the last few years up to a quite mature level.
Has your organization compliance with ISMS? A case study in an Iranian Bank
Venue: arXiv cs.CY 2013
Year: 2013

The purpose of this study is proposing a model to determine the gaps between security standards requirements and the reality of implementation ISMS. The research approach analyzes the various industry standards relevant to information security and responses gained from interviewing with 45 individuals of IT professionals and information security experts (who are chosen with targeted sampling) in order to develop a model comprising factors and subfactors which assesses compliance with ISMS (Information Security Management System) in organizations. For hypothesis test, binomial test and for ranking of factors and sub factors, Friedman test was done. This model tested in a bank and the degree of compliance with ISMS calculated.
A Gaps Approach to Access the Efficiency and Effectiveness of IT-Initiatives In Rural Areas: case study of Samalta, a village in the central Himalayan Region of India
Venue: arXiv cs.CY 2013
Year: 2013

This paper focuses on the effectiveness and efficiency of IT initiatives in rural areas where topology creates isolation to developmental activities. A village is selected for the study and information is gathered through interviews of village dwellers. These collected responses are then analyzed and a gaps model is proposed.
Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder
Venue: arXiv cs.CV 2013
Year: 2013

Nuclear image has emerged as a promising research work in medical field. Images from different modality meet its own challenge. Positron Emission Tomography (PET) image may help to precisely localize disease to assist in planning the right treatment for each case and saving valuable time. In this paper, a novel approach of Spatial Fuzzy C Means (PET SFCM) clustering algorithm is introduced on PET scan image datasets. The proposed algorithm is incorporated the spatial neighborhood information with traditional FCM and updating the objective function of each cluster. This algorithm is implemented and tested on huge data collection of patients with brain neuro degenerative disorder such as Alzheimers disease. It has demonstrated its effectiveness by testing it for real world patient data sets. Experimental results are compared with conventional FCM and K Means clustering algorithm. The performance of the PET SFCM provides satisfactory results compared with other two algorithms
Query Expansion Using Term Distribution and Term Association
Venue: arXiv cs.IR 2013
Year: 2013

Good term selection is an important issue for an automatic query expansion (AQE) technique. AQE techniques that select expansion terms from the target corpus usually do so in one of two ways. Distribution based term selection compares the distribution of a term in the (pseudo) relevant documents with that in the whole corpus / random distribution. Two well-known distribution-based methods are based on Kullback-Leibler Divergence (KLD) and Bose-Einstein statistics (Bo1). Association based term selection, on the other hand, uses information about how a candidate term co-occurs with the original query terms. Local Context Analysis (LCA) and Relevance-based Language Model (RM3) are examples of association-based methods. Our goal in this study is to investigate how these two classes of methods may be combined to improve retrieval effectiveness. We propose the following combination-based approach. Candidate expansion terms are first obtained using a distribution based method. This set is then refined based on the strength of the association of terms with the original query terms. We test our methods on 11 TREC collections. The proposed combinations generally yield better results than each individual method, as well as other state-of-the-art AQE approaches. En route to our primary goal, we also propose some modifications to LCA and Bo1 which lead to improved performance.
A Technique for Deriving One-Shot Achievability Results in Network Information Theory
Venue: arXiv cs.IT 2013
Year: 2013

This paper proposes a novel technique to prove a one-shot version of achievability results in network information theory. The technique is not based on covering and packing lemmas. In this technique, we use an stochastic encoder and decoder with a particular structure for coding that resembles both the ML and the joint-typicality coders. Although stochastic encoders and decoders do not usually enhance the capacity region, their use simplifies the analysis. The Jensen inequality lies at the heart of error analysis, which enables us to deal with the expectation of many terms coming from stochastic encoders and decoders at once. The technique is illustrated via several examples: point-to-point channel coding, Gelfand-Pinsker, Broadcast channel (Marton), Berger-Tung, Heegard-Berger/Kaspi, Multiple description coding and Joint source-channel coding over a MAC. Most of our one-shot results are new. The asymptotic forms of these expressions is the same as that of classical results. Our one-shot bounds in conjunction with multi-dimensional Berry-Essen CLT imply new results in the finite blocklength regime. In particular applying the one-shot result for the memoryless broadcast channel in the asymptotic case, we get the entire region of Marton's inner bound without any need for time-sharing.
Bibliometrics for Internet Media: Applying the h-Index to YouTube
Venue: arXiv cs.DL 2013
Year: 2013

The h-index can be a useful metric for evaluating a person's output of Internet media. Here we advocate and demonstrate adaption of the h-index and the g-index to the top video content creators on YouTube. The h-index for Internet video media is based on videos and their view counts. The index h is defined as the number of videos with >= h*10^5 views. The index g is defined as the number of videos with >= g*10^5 views on average. When compared to a video creator's total view count, the h-index and g-index better capture both productivity and impact in a single metric.
Concurrent Game Structures with Roles
Venue: arXiv cs.LO 2013
Year: 2013

In the following paper we present a new semantics for the well-known strategic logic ATL. It is based on adding roles to concurrent game structures, that is at every state, each agent belongs to exactly one role, and the role specifies what actions are available to him at that state. We show advantages of the new semantics, provide motivating examples based on sensor networks, and analyze model checking complexity.
Adaptive Partitioning and its Applicability to a Highly Scalable and Available Geo-Spatial Indexing Solution
Venue: arXiv cs.DB 2013
Year: 2013

Satellite Tracking of People (STOP) tracks thousands of GPS-enabled devices 24 hours a day and 365 days a year. With locations captured for each device every minute, STOP servers receive tens of millions of points each day. In addition to cataloging these points in real-time, STOP must also respond to questions from customers such as, "What devices of mine were at this location two months ago?" They often then broaden their question to one such as, "Which of my devices have ever been at this location?" The processing requirements necessary to answer these questions while continuing to process inbound data in real-time is non-trivial. To meet this demand, STOP developed Adaptive Partitioning to provide a cost-effective and highly available hardware platform for the geographical and time-spatial indexing capabilities necessary for responding to customer data requests while continuing to catalog inbound data in real-time.
Securing Web Services Using XML Signature and XML Encryption
Venue: arXiv cs.CR 2013
Year: 2013

This paper is aimed to evaluate the importance of XML Signature and XML Encryption in Web Service Security. In today's business scenario, organizations are investing huge amount of resources in Web Services. Web Service Transactions are done mainly through plain-text XML formats like SOAP and WSDL, hence hacking into them is not a tedious task. XML Signature and XML Encryption ensure security to XML documents as well as retain the structure of documents, thereby making it easy to implement them. These two methods are evaluated on the parameters of authentication, authorization, integration, confidentiality and non-repudiation.
A New Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications
Venue: arXiv cs.IT 2013
Year: 2013

The existing upper and lower bounds between entropy and error are mostly derived through an inequality means without linking to joint distributions. In fact, from either theoretical or application viewpoint, there exists a need to achieve a complete set of interpretations to the bounds in relation to joint distributions. For this reason, in this work we propose a new approach of deriving the bounds between entropy and error from a joint distribution. The specific case study is given on binary classifications, which can justify the need of the proposed approach. Two basic types of classification errors are investigated, namely, the Bayesian and non-Bayesian errors. For both errors, we derive the closed-form expressions of upper bound and lower bound in relation to joint distributions. The solutions show that Fano's lower bound is an exact bound for any type of errors in a relation diagram of "Error Probability vs. Conditional Entropy". A new upper bound for the Bayesian error is derived with respect to the minimum prior probability, which is generally tighter than Kovalevskij's upper bound.
Efficient Separability of Regular Languages by Subsequences and Suffixes
Venue: arXiv cs.FL 2013
Year: 2013

When can two regular word languages K and L be separated by a simple language? We investigate this question and consider separation by piecewise- and suffix-testable languages and variants thereof. We give characterizations of when two languages can be separated and present an overview of when these problems can be decided in polynomial time if K and L are given by nondeterministic automata.
Anytime Reliable LDPC Convolutional Codes for Networked Control over Wireless Channel
Venue: arXiv cs.IT 2013
Year: 2013

This paper deals with the problem of stabilizing an unstable system through networked control over the wireless medium. In such a situation a remote sensor communicates the measurements to the system controller through a noisy channel. In particular, in the AWGN scenario, we show that protograph-based LDPC convolutional codes achieve anytime reliability and we also derive a lower bound to the signal-to-noise ratio required to stabilize the system. Moreover, on the Rayleigh-fading channel, we show by simulations that resorting to multiple sensors allows to achieve a diversity gain.
A Genetic algorithm to solve the container storage space allocation problem
Venue: arXiv cs.NE 2013
Year: 2013

This paper presented a genetic algorithm (GA) to solve the container storage problem in the port. This problem is studied with different container types such as regular, open side, open top, tank, empty and refrigerated containers. The objective of this problem is to determine an optimal containers arrangement, which respects customers delivery deadlines, reduces the rehandle operations of containers and minimizes the stop time of the container ship. In this paper, an adaptation of the genetic algorithm to the container storage problem is detailed and some experimental results are presented and discussed. The proposed approach was compared to a Last In First Out (LIFO) algorithm applied to the same problem and has recorded good results
Grid Minors in Damaged Grids
Venue: arXiv cs.DM 2013
Year: 2013

We prove upper and lower bounds on the size of the largest square grid graph that is a subgraph, minor, or shallow minor of a graph in the form of a larger square grid from which a specified number of vertices have been deleted. Our bounds are tight to within constant factors. We also provide less-tight bounds on analogous problems for higher-dimensional grids.
Equilateral L-Contact Graphs
Venue: arXiv cs.CG 2013
Year: 2013

We consider {\em L-graphs}, that is contact graphs of axis-aligned L-shapes in the plane, all with the same rotation. We provide several characterizations of L-graphs, drawing connections to Schnyder realizers and canonical orders of maximally planar graphs. We show that every contact system of L's can always be converted to an equivalent one with equilateral L's. This can be used to show a stronger version of a result of Thomassen, namely, that every planar graph can be represented as a contact system of square-based cuboids. We also study a slightly more restricted version of equilateral L-contact systems and show that these are equivalent to homothetic triangle contact representations of maximally planar graphs. We believe that this new interpretation of the problem might allow for efficient algorithms to find homothetic triangle contact representations, that do not use Schramm's monster packing theorem.
Joint Ultra-wideband and Signal Strength-based Through-building Tracking for Tactical Operations
Venue: arXiv cs.OH 2013
Year: 2013

Accurate device free localization (DFL) based on received signal strength (RSS) measurements requires placement of radio transceivers on all sides of the target area. Accuracy degrades dramatically if sensors do not surround the area. However, law enforcement officers sometimes face situations where it is not possible or practical to place sensors on all sides of the target room or building. For example, for an armed subject barricaded in a motel room, police may be able to place sensors in adjacent rooms, but not in front of the room, where the subject would see them. In this paper, we show that using two ultra-wideband (UWB) impulse radios, in addition to multiple RSS sensors, improves the localization accuracy, particularly on the axis where no sensors are placed (which we call the x-axis). We introduce three methods for combining the RSS and UWB data. By using UWB radios together with RSS sensors, it is still possible to localize a person through walls even when the devices are placed only on two sides of the target area. Including the data from the UWB radios can reduce the localization area of uncertainty by more than 60%.
Statistical Proof Pattern Recognition: Automated or Interactive?
Venue: arXiv cs.SE 2013
Year: 2013

In this paper, we compare different existing approaches employed in data mining of big proof libraries in automated and interactive theorem proving.
Causality in Bayesian Belief Networks
Venue: arXiv cs.AI 2013
Year: 2013

We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise when mechanisms are placed in the context of a system. We lay the link between structural equations models and BBNs models and formulate the conditions under which the latter can be given causal interpretation.
End-User Construction of Influence Diagrams for Bayesian Statistics
Venue: arXiv cs.AI 2013
Year: 2013

Influence diagrams are ideal knowledge representations for Bayesian statistical models. However, these diagrams are difficult for end users to interpret and to manipulate. We present a user-based architecture that enables end users to create and to manipulate the knowledge representation. We use the problem of physicians' interpretation of two-arm parallel randomized clinical trials (TAPRCT) to illustrate the architecture and its use. There are three primary data structures. Elements of statistical models are encoded as subgraphs of a restricted class of influence diagram. The interpretations of those elements are mapped into users' language in a domain-specific, user-based semantic interface, called a patient-flow diagram, in the TAPRCT problem. Pennitted transformations of the statistical model that maintain the semantic relationships of the model are encoded in a metadata-state diagram, called the cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system uses modular actions called construction steps. This framework has been implemented in a system called THOMAS, that allows physicians to interpret the data reported from a TAPRCT.
Additive Belief-Network Models
Venue: arXiv cs.AI 2013
Year: 2013

The inherent intractability of probabilistic inference has hindered the application of belief networks to large domains. Noisy OR-gates [30] and probabilistic similarity networks [18, 17] escape the complexity of inference by restricting model expressiveness. Recent work in the application of belief-network models to time-series analysis and forecasting [9, 10] has given rise to the additive belief network model (ABNM). We (1) discuss the nature and implications of the approximations made by an additive decomposition of a belief network, (2) show greater efficiency in the induction of additive models when available data are scarce, (3) generalize probabilistic inference algorithms to exploit the additive decomposition of ABNMs, (4) show greater efficiency of inference, and (5) compare results on inference with a simple additive belief network.
Probabilistic Conceptual Network: A Belief Representation Scheme for Utility-Based Categorization
Venue: arXiv cs.AI 2013
Year: 2013

Probabilistic conceptual network is a knowledge representation scheme designed for reasoning about concepts and categorical abstractions in utility-based categorization. The scheme combines the formalisms of abstraction and inheritance hierarchies from artificial intelligence, and probabilistic networks from decision analysis. It provides a common framework for representing conceptual knowledge, hierarchical knowledge, and uncertainty. It facilitates dynamic construction of categorization decision models at varying levels of abstraction. The scheme is applied to an automated machining problem for reasoning about the state of the machine at varying levels of abstraction in support of actions for maintaining competitiveness of the plant.
Reasoning about the Value of Decision-Model Refinement: Methods and Application
Venue: arXiv cs.AI 2013
Year: 2013

We investigate the value of extending the completeness of a decision model along different dimensions of refinement. Specifically, we analyze the expected value of quantitative, conceptual, and structural refinement of decision models. We illustrate the key dimensions of refinement with examples. The analyses of value of model refinement can be used to focus the attention of an analyst or an automated reasoning system on extensions of a decision model associated with the greatest expected value.
A Generalization of the Noisy-Or Model
Venue: arXiv cs.AI 2013
Year: 2013

The Noisy-Or model is convenient for describing a class of uncertain relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or model for Boolean variables. Here we generalize the model to nary input and output variables and to arbitrary functions other than the Boolean OR function. This generalization is a useful modeling aid for construction of Bayesian networks. We illustrate with some examples including digital circuit diagnosis and network reliability analysis.
An efficient approach for finding the MPE in belief networks
Venue: arXiv cs.AI 2013
Year: 2013

Given a belief network with evidence, the task of finding the I most probable explanations (MPE) in the belief network is that of identifying and ordering the I most probable instantiations of the non-evidence nodes of the belief network. Although many approaches have been proposed for solving this problem, most work only for restricted topologies (i.e., singly connected belief networks). In this paper, we will present a new approach for finding I MPEs in an arbitrary belief network. First, we will present an algorithm for finding the MPE in a belief network. Then, we will present a linear time algorithm for finding the next MPE after finding the first MPE. And finally, we will discuss the problem of finding the MPE for a subset of variables of a belief network, and show that the problem can be efficiently solved by this approach.